System for taking into account micro wind conditions in flight plans for aerial vehicles

ABSTRACT

A system for taking into account micro wind conditions in a region. The system comprises a plurality of aerial vehicles within the region and a wind speed calculator. Each of the plurality of aerial vehicles has an altitude sensor and a GPS receiver. The wind speed calculator is configured to determine wind vectors within the region using measurements from the plurality of aerial vehicles.

BACKGROUND INFORMATION 1. Field

The present disclosure relates generally to operating aerial vehiclesand, more specifically, to taking into account micro wind conditionswhen operating aerial vehicles.

2. Background

Flight plans for commercial aircraft are planned with coarsely-grainedwind forecasts generated by weather entities, such as the NationalWeather Service. Coarsely-grained wind forecasts are commonly generatedwith a 0.5° lateral resolution, as well as a 50 millibar verticalresolution. While this resolution is sufficient for commercial aircraftcovering thousands of miles of distance, this resolution is not able tocapture the “microscopic” winds in between the resolutionpoints/coordinates.

Unmanned aerial vehicles typically fly significantly shorter distancesthan commercial aircraft. Some unmanned aerial vehicles delivering cargoor taxiing passengers may travel only within the region bounded by a0.5° lateral resolution. Due to the resolution, coarsely-grained windforecasts do not provide details of the weather along flight paths forthese unmanned aerial vehicles. For example, wind vectors along theflight paths are unknown.

Some unmanned aerial vehicles may fly at altitudes considerably higherthan weather gauges. Unmanned aerial vehicles fly at altitudes lowerthan cruising altitudes for commercial aircraft. Wind speed anddirection change with altitude. Wind vectors and directions gathered atthe weather gauges may not be desirable for forming flight plans forunmanned aerial vehicles. Wind vectors and directions gathered fromcommercial aircraft may not be desirable for forming flight plans forunmanned aerial vehicles.

Therefore, it would be desirable to have a method and apparatus thattake into account at least some of the issues discussed above, as wellas other possible issues. For example, it would be desirable to have amethod and apparatus that aids in flying unmanned aerial vehicles in aregion. As another example, it would be desirable to have a method andapparatus that create flight plans for unmanned aerial vehicles thattake into account conditions within a region.

SUMMARY

An illustrative embodiment of the present disclosure provides a systemfor taking into account micro wind conditions in a region. The systemcomprises a plurality of aerial vehicles within the region and a windspeed calculator. Each of the plurality of aerial vehicles has analtitude sensor and a GPS receiver. The wind speed calculator isconfigured to determine wind vectors within the region usingmeasurements from the plurality of aerial vehicles.

Another illustrative embodiment of the present disclosure provides amethod. Altitude measurements are collected for a plurality of aerialvehicles while the plurality of aerial vehicles is flying in a region.Wind vectors within the region are determined using the plurality ofaerial vehicles.

A further illustrative embodiment of the present disclosure provides amethod. Wind vectors within a region at a first time are determinedusing a plurality of aerial vehicles flying in the region. Athree-dimensional wind map of the region is generated, includinginterpolated wind vectors based on the wind vectors. Thethree-dimensional wind map is correlated with a coarsely-grainedforecast for the region at the first time. A three-dimensional model ofthe region is trained with the three-dimensional wind map correlatedwith the coarsely-grained forecast for the region at the first time. Acoarsely-grained forecast for the region at a second time is received. Athree-dimensional wind prediction map for the region at the second timeis created.

The features and functions can be achieved independently in variousembodiments of the present disclosure or may be combined in yet otherembodiments in which further details can be seen with reference to thefollowing description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the illustrativeembodiments are set forth in the appended claims. The illustrativeembodiments, however, as well as a preferred mode of use, furtherobjectives and features thereof, will best be understood by reference tothe following detailed description of an illustrative embodiment of thepresent disclosure when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is an illustration of a block diagram of an environment in whichan unmanned aerial vehicle flies using a flight plan taking into accountmicro wind conditions in accordance with an illustrative embodiment;

FIG. 2 is an illustration of a two-dimensional view of locations for aplurality of aerial vehicles identified in a region in accordance withan illustrative embodiment;

FIG. 3 is an illustration of a three-dimensional view of locations for aplurality of aerial vehicles identified in a region in accordance withan illustrative embodiment;

FIG. 4 is an illustration of a three-dimensional view of determined windvectors in a region in accordance with an illustrative embodiment;

FIG. 5 is an illustration of an unmanned aerial vehicle with exemplaryvectors in accordance with an illustrative embodiment;

FIG. 6 is an illustration of a two-dimensional view of set grid pointsin a region in accordance with an illustrative embodiment;

FIG. 7 is an illustration of a three-dimensional view of set grid pointsin a region in accordance with an illustrative embodiment;

FIG. 8 is an illustration of a two-dimensional view of interpolated windvectors at set grid points in a region in accordance with anillustrative embodiment;

FIG. 9 is an illustration of a two-dimensional view of an unmannedaerial vehicle with an original flight plan and a new flight plan takinginto account micro wind conditions in accordance with an illustrativeembodiment;

FIG. 10 is an illustration of differences between an actual track and aplanned track for an unmanned aerial vehicle;

FIGS. 11A and 11B are an illustration of a flowchart of a method forflying an aerial vehicle in a region based on wind vectors determined inthe region in accordance with an illustrative embodiment; and

FIG. 12 is an illustration of a flowchart of a method for flying anaerial vehicle in a region in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION

The illustrative embodiments recognize and take into account one or moredifferent considerations. For example, the illustrative embodimentsrecognize and take into account that unmanned aerial vehicles areadvantageous in several scenarios. The illustrative embodimentsrecognize and take into account that unmanned aerial vehicles can beused for delivery of packages by a store or vendor. The illustrativeembodiments recognize and take into account that unmanned aerialvehicles can be used for delivery of fast food orders. The illustrativeembodiments recognize and take into account that unmanned aerialvehicles can be used for transport of human or animal passengers.

The illustrative embodiments recognize and take into account thatunmanned aerial vehicles (UAVs) need to file flight plans. Theillustrative embodiments recognize and take into account that becausethe flights of unmanned aerial vehicles cover shorter distances, thecoarsely granular nature of wind forecasts do not desirably aid indeveloping flight plans for unmanned aerial vehicles. For example,coarsely-grained wind forecasts do not provide a desirable amount ofinformation for determining the most cost-effective route.

The illustrative embodiments recognize and take into account that itwould be desirable to provide a three-dimensional “live” or real-timeview of winds in a region. The illustrative embodiments furtherrecognize and take into account that a plurality of unmanned aerialvehicles in a region can be used to collect measurements to determinewind vectors within the region. The illustrative embodiments alsorecognize and take into account that the real-time view of the winds inthe region may be used to create a wind prediction for a future time.The illustrative embodiments additionally recognize and take intoaccount that wind vectors at a pre-determined grid may be determinedusing the real-time view of the winds.

The illustrative embodiments also recognize and take into account thatturbulence is undesirable for unmanned aerial vehicles. The illustrativeembodiments also recognize and take into account that it is desirable tobe able to pre-determine corrections that an unmanned aerial vehicleshould perform to reduce encountered turbulence by the unmanned aerialvehicle.

Referring now to the figures and, in particular, with reference to FIG.1, an illustration of a block diagram of an environment in which anunmanned aerial vehicle flies using a flight plan taking into accountmicro wind conditions is depicted in accordance with an illustrativeembodiment. Environment 100 contains system 102 for taking into accountmicro wind conditions in region 104. In some illustrative examples,region 104 is within a single 0.5° lateral resolution grid.

In some illustrative examples, region 104 is at least one of suburbanregion 105 or urban region 107. In some illustrative examples, region104 is a city 103. City 103 includes at least one of suburban region 105or urban region 107.

System 102 comprises plurality of aerial vehicles 109 within region 104and wind speed calculator 108. In some illustrative examples, system 102also comprises flight plan generator 110. In some illustrative examples,plurality of aerial vehicles 109 comprises plurality of unmanned aerialvehicles 106.

Wind speed calculator 108 is configured to determine wind vectors 111within region 104 using measurements 112 from plurality of aerialvehicles 109. When plurality of aerial vehicles 109 comprises pluralityof unmanned aerial vehicles 106, wind speed calculator 108 is configuredto determine wind vectors 111 within region 104 using measurements 112from plurality of unmanned aerial vehicles 106.

Flight plan generator 110 is configured to create flight plan 114 withinregion 104 for aerial vehicle 115 based on wind vectors 111 determinedby wind speed calculator 108. As used herein, the terms “flight plan”and “flight path,” may be used interchangeably. When aerial vehicle 115takes the form of unmanned aerial vehicle 116, flight plan generator 110is configured to create flight plan 114 within region 104 for unmannedaerial vehicle 116 based on wind vectors 111 determined by wind speedcalculator 108.

Each of plurality of aerial vehicles 109 has an altitude sensor and aGPS receiver. As depicted, plurality of aerial vehicles 109 has sensors118, including GPS receivers 120 and altitude sensors 122. In someillustrative examples, sensors 118 on plurality of aerial vehicles 109will include other desirable sensors. In some illustrative examples,plurality of aerial vehicles 109 also includes wind speed sensors 124.

When plurality of aerial vehicles 109 comprises plurality of unmannedaerial vehicles 106, each of plurality of unmanned aerial vehicles 106has sensors 118. For example, when present, each of plurality ofunmanned aerial vehicles 106 has an altitude sensor and a GPS receiver.

Measurements 112 are associated with first time 126. Measurements 112are obtained using sensors 118.

Wind vectors 111 within region 104 at first time 126 are determinedusing measurements 112 from plurality of aerial vehicles 109. In someillustrative examples, the micro winds within region 104 are directlymeasured from plurality of aerial vehicles 109. In these illustrativeexamples, measurements 112 include wind measurements 128 taken by windspeed sensors 124. In these illustrative examples, wind speed calculator108 associates wind measurements 128 with locations 130 and altitudes132 of plurality of aerial vehicles 109 to form wind vectors 111.

In some other illustrative examples, the micro winds within region 104are indirectly measured using plurality of aerial vehicles 109. In someillustrative examples, wind vectors 111 are determined usingcalculations and measurements 112. In these illustrative examples,measurements 112 include set speeds 134 and set headings 136. In someillustrative examples, set speeds 134 and set headings 136 at first time126 are provided from flight plans of plurality of aerial vehicles 109.In some illustrative examples, set speeds 134 and set headings 136 atfirst time 126 are provided from controllers of plurality of aerialvehicles 109.

In some illustrative examples, measurements 112 include observed speeds138 and observed headings 140. Observed speeds 138 and observed headings140 may be determined relative to ground 142 in region 104. In someillustrative examples, observed speeds 138 and observed headings 140 aredetermined using GPS receivers 120 of plurality of aerial vehicles 109.

In some illustrative examples, wind speed calculator 108 is configuredto determine wind vectors 111 using vector addition, set speeds 134 ofplurality of aerial vehicles 109, set headings 136 of plurality ofaerial vehicles 109, observed speeds 138 of plurality of aerial vehicles109, and observed headings 140 of plurality of aerial vehicles 109.

Wind vectors 111 are determined for micro winds in region 104 at firsttime 126. Wind vectors 111 are located at locations 130 of plurality ofaerial vehicles 109 at first time 126. Wind vectors 111 are saved todatabase 144 of system 102.

Database 144 also includes coarsely-grained forecasts 146.Coarsely-grained forecasts 146 are forecasts for region 104. Asdepicted, coarsely-grained forecasts 146 includes coarsely-grainedforecast 148 at first time 126 and coarsely-grained forecast 150 atsecond time 152.

Information from database 144 is introduced to real-time wind analysisapparatus 154. Information from database 144 is used by real-time windanalysis apparatus 154 to train three-dimensional model 156 of region104. For example, wind vectors 111 at first time 126 andcoarsely-grained forecast 148 at first time 126 may be provided formodel training system 158 to train three-dimensional model 156.

Three-dimensional model 156 is a representation of region 104.Three-dimensional model 156 includes any desirable features of region104. In some illustrative examples, three-dimensional model 156 includesbuildings. In some illustrative examples, three-dimensional model 156includes vegetation. Some features of three-dimensional model 156 maychange over time. For example, buildings may be built or removed fromregion 104 over time. As another example, leaves from trees or othervegetation in region 104 may not be present during the fall and wintermonths. As yet another example, temporary structures may be erected andthen removed within region 104.

Model training system 158 may make modifications to three-dimensionalmodel 156 based on input from database 144. For example, model trainingsystem 158 may modify three-dimensional model 156 based on acoarsely-grained forecast of coarsely-grained forecasts 146 and windvectors determined by wind speed calculator 108 and correlated to thatcoarsely grained forecast. In one example, model training system 158 maymodify three-dimensional model 156 based on coarsely-grained forecast148 of coarsely-grained forecasts 146 and wind vectors 111 determined bywind speed calculator 108 and correlated to coarsely grained forecast148. Modifications to three-dimensional model 156 take into accountchanges within region 104, such as any changes to buildings orvegetation present in region 104.

Information from database 144 is used by three-dimensional wind mapgenerator 160 to generate a three-dimensional wind map of micro windswithin region 104. Three-dimensional wind map generator 160 uses inputfrom database 144 and three-dimensional model 156 to generate athree-dimensional wind map.

In one illustrative example, three-dimensional wind map generator 160generates three-dimensional wind map 162 for first time 126.Three-dimensional wind map 162 may be referred to as a “real-time” orcurrent wind map. Three-dimensional wind map 162 includes interpolatedwind vectors 164. Interpolated wind vectors 164 are associated with setgrid points within region 104. Interpolated wind vectors 164 are onthree-dimensional grid 166. Interpolated wind vectors 164 are associatedwith set grid points of three-dimensional grid 166 within region 104.Three-dimensional wind map 162 of region 104 is generated includinginterpolated wind vectors 164 based on wind vectors 111.

Three-dimensional grid 166 is a grid in both lateral and verticaldimensions. Three-dimensional grid 166 explicitly defines locations bylatitude/longitude/altitude. Locations 130 of wind vectors 111 arescattered throughout region 104 based on assigned operations and flightpaths of plurality of aerial vehicles 109. By tailoring wind vectors 111to a grid, such as three-dimensional grid 166, wind vectors 111 may beused in training using model training system 158. The tailoring processmay be described as interpolation between wind vectors 111 so thatinterpolated wind vectors 164 at each grid point of three-dimensionalgrid 166 are calculated. In some illustrative examples, wind vectors,such as interpolated wind vectors 164, on a lateral scale are calculatedat each grid point. In some illustrative examples, wind vectors, such asinterpolated wind vectors 164, on a lateral scale, as well as atdifferent altitudes, are calculated at each grid point.

Interpolated wind vectors 164 along locations in three-dimensional grid166 are determined through interpolation. As a result, interpolated windvectors 164 includes a wind speed at each coordinate point ofthree-dimensional grid 166. This is performed for all points ofthree-dimensional grid 166.

In another example, three-dimensional wind map generator 160 generatesthree-dimensional wind prediction map 168. Three-dimensional windprediction map 168 is a map for predicted wind vectors 170 at secondtime 152. Second time 152 is a future time. Second time 152 occurs afterfirst time 126.

Predicted wind vectors 170 are wind vectors at each point ofthree-dimensional grid 166 at second time 152. As depicted,three-dimensional wind map 162 at first time 126 and three-dimensionalwind prediction map 168 at second time 152 have the samethree-dimensional grid, three-dimensional grid 166. Predicted windvectors 170 are determined using three-dimensional model 156 andcoarsely-grained forecast 150 at second time 152.

Flight plan generator 110 generates flight plans using three-dimensionalwind maps generated by three-dimensional wind map generator 160. In someillustrative examples, flight plan generator 110 generates flight plansusing three-dimensional wind map 162 for first time 126. In someillustrative examples, flight plan generator 110 generates flight plansusing a three-dimensional wind prediction map for a future time such asthree-dimensional wind prediction map 168 at second time 152.

In some illustrative examples, flight plan generator 110 may generatenew flight plans prior to takeoff. For example, flight plan generator110 may create flight plan 114 for unmanned aerial vehicle 116 prior totakeoff of unmanned aerial vehicle 116. In some illustrative examples,flight plan generator 110 may generate modified flight plans duringflight of a respective unmanned aerial vehicle. For example, flight plangenerator 110 may create modified flight plan 172 for unmanned aerialvehicle 116 as unmanned aerial vehicle 116 flies through region 104.

Flight plan 114 takes into account micro winds within region 104. Flightplan 114 takes into account any desirable parameters of at least one ofunmanned aerial vehicle 116 or cargo 174. For example, flight plan 114may take into account at least one of fuel efficiency, turbulence,maximum altitude, maximum speed of unmanned aerial vehicle 116,dimensions of unmanned aerial vehicle 116, order parameters, cargo type,or any other desirable parameters.

In some illustrative examples, flight plan generator 110 is configuredto determine maximum acceptable turbulence 178 for cargo 174 of unmannedaerial vehicle 116 and plan flight plan 114 such that unmanned aerialvehicle 116 is projected to encounter turbulence below maximumacceptable turbulence 178. In some illustrative examples, flight plangenerator 110 is configured to determine deliver by time 180 for cargo174 of unmanned aerial vehicle 116, and plan flight plan 114 such thatunmanned aerial vehicle 116 is projected to deliver cargo 174 prior todeliver by time 180.

In some illustrative examples, flight plan generator 110 is configuredto create flight plan 114 using three-dimensional wind prediction map168 for region 104 for a future time. In some illustrative examples,three-dimensional wind map generator 160 is configured to generate athree-dimensional wind prediction map for region 104 for a future timeusing three-dimensional model 156 of region 104 and a coarsely-grainedforecast for the future time. For example, three-dimensional wind mapgenerator 160 is configured to generate three-dimensional windprediction map 168 for region 104 for second time 152 usingthree-dimensional model 156 of region 104 and coarsely-grained forecast150 for second time 152.

In some illustrative examples, model training system 158 is configuredto continuously check three-dimensional model 156. For example, modeltraining system 158 may verify appropriate outputs as new inputs arereceived. Model training system 158 is configured to refine and updatethree-dimensional model 156 of region 104 using additional determinedwind vectors 176 and received coarsely-grained forecasts 146 for region104.

System 102 includes communication system 182 configured to communicateflight plans with plurality of aerial vehicles 109. Communication system182 communicates flight plan 114 with unmanned aerial vehicle 116.

During operation of system 102, real-time wind measurements/reports,such as wind vectors 111, are first matched with their respectivecoarsely-grained wind forecast (from e.g. the National Weather Service).For example, winds at 3 pm are matched to their 3 pm forecast. In someillustrative examples, the forecast may be published prior to the validtime. For instance, a forecast published at 1 pm may be valid at 3 pm.

After matching, the instance pair formed by matching wind vectors 111with coarsely grained forecast 148 is saved to database 144. Thisinstance pair will then be used in predictive real-time model/machinelearning algorithm training, such as by model training system 158,occurring in real-time wind analysis apparatus 154. In some illustrativeexamples, the algorithms are used to predict a future three-dimensionalview of the winds when a new coarsely-grained forecast arrives. Anotheroutput of real-time wind analysis apparatus 154 may be thecurrent/live/real-time 3D wind speed view. These two outputs can, in thefollowing, be used to either plan flights “right now,” i.e. with thecurrent micro live wind situation or to plan flights at a future timeusing the predicted micro wind situation

At any time t, wind vectors 111 are determined using plurality of aerialvehicles 109 present in region 104, with this information relayed toreal-time wind analysis apparatus 154. Upon arrival, interpolated windvectors 164 along the locations in the defined grid, three-dimensionalgrid 166, are determined through interpolation. Using interpolation,interpolated wind vectors 164 valid at each coordinate point ofthree-dimensional grid 166 are known. This is performed for all pointsof three-dimensional grid 166. Should a coarsely-grained forecast beavailable for this current point in time, it is then matched with thisthree-dimensional wind map 162 and saved in database 144.

Following matching a three-dimensional wind map with a coarsely grainedforecast, this match is transferred from the database to real-time windanalysis apparatus 154. The matches or, “instance pairs,” arerecurrently used to train machine learning algorithms in model trainingsystem 158. A Lambda architecture can be employed, which ensures areal-time algorithm training using streams of data.

In some illustrative examples, real-time wind analysis apparatus 154 maybe used to form a three-dimensional wind prediction map, such asthree-dimensional wind prediction map 168, in response to receiving anew coarsely-grained forecast. When a new coarsely-grained forecastarrives, which forecasts wind values at some point in the future, t2,this forecast is then applied to the machine learning algorithms, whichthen generate a prediction for three-dimensional wind prediction map 168in region 104, valid for time t2. This also resembles the output of theapparatus and serves as input to flight plan generator 110 generatingflight plans for unmanned aerial vehicles flying in region 104.

Real-time wind analysis apparatus 154 may be implemented in at least oneof hardware or software. As depicted, real-time wind analysis apparatus154 is implemented in computer system 184. As depicted, computer system184 is not present within region 104. However, in other illustrativeexamples, computer system 184 may be present within region 104.

As used herein, the phrase “at least one of,” when used with a list ofitems, means different combinations of one or more of the listed itemsmay be used, and only one of each item in the list may be needed. Inother words, “at least one of” means any combination of items and numberof items may be used from the list, but not all of the items in the listare required. The item may be a particular object, a thing, or acategory.

For example, “at least one of item A, item B, or item C” may include,without limitation, item A, item A and item B, or item B. This examplealso may include item A, item B, and item C, or item B and item C. Ofcourse, any combination of these items may be present. In otherexamples, “at least one of” may be, for example, without limitation, twoof item A, one of item B, and ten of item C; four of item B and seven ofitem C; or other suitable combinations.

The illustration of environment 100 in FIG. 1 is not meant to implyphysical or architectural limitations to the manner in which anillustrative embodiment may be implemented. Other components in additionto or in place of the ones illustrated may be used. Some components maybe unnecessary. Also, the blocks are presented to illustrate somefunctional components. One or more of these blocks may be combined,divided, or combined and divided into different blocks when implementedin an illustrative embodiment.

For example, wind speed calculator 108 may receive additionalmeasurements from other equipment or structures than plurality of aerialvehicles 109. In some illustrative examples, wind speed calculator 108receives measurements 186 from weather stations 188. In theseillustrative examples, wind speed calculator 108 is configured todetermine wind vectors 111 within region 104 using measurements 112 fromplurality of aerial vehicles 109 and measurements 186 from weatherstations 188.

Weather stations 188 are at fixed locations within region 104. For eachweather station of weather stations 188, the respective latitude,respective longitude, and respective altitude does not change. In someillustrative examples, each of measurements 186 includes the respectivelatitude, respective longitude, and respective altitude for therespective measurement. In other illustrative examples, each ofmeasurements 186 includes an identification number for a respectiveweather station of weather stations 188. The identification number maybe correlated to a respective latitude, respective longitude, andrespective altitude for the respective weather station by wind speedcalculator 108.

Turning now to FIG. 2, an illustration of a two-dimensional view oflocations for a plurality of aerial vehicles identified in a region isdepicted in accordance with an illustrative embodiment. Region 200 is aphysical implementation of region 104 of FIG. 1. As depicted, region 200includes at least one of a suburban region or an urban region. Asdepicted, region 200 includes a city.

Region 200 is positioned between marker 202, marker 204, marker 206, andmarker 208. In some illustrative examples, region 200 is within a single0.5° lateral resolution grid. In these illustrative examples, marker202, marker 204, marker 206, and marker 208 identify the single 0.5°lateral resolution grid. While a forecast will exist at each of thecoordinates, marker 202, marker 204, marker 206, and marker 208, thewind situation in between them is unknown.

Assuming an operator of an unmanned aerial vehicle wants to deliverparcels to homes in the city within region 200, the operator would liketo know wind vectors and wind directions within the city. Using the windvectors and wind directions within the city, the operator may plan moredesirable flight routes. For example, using the wind vectors and winddirections within the city, the operator may plan flight plans withreduced turbulence. As another example, using the wind vectors and winddirections within the city, the operator may plan flight plans withreduced fuel usage. As yet another example, using the wind vectors andwind directions within the city, the operator may plan flight plans withreduced flight time.

As depicted, plurality of points 210 are present within region 200.Plurality of points 210 represent positions of aerial vehicles flyingwithin region 200. The aerial vehicles are physical implementations ofplurality of aerial vehicles 109 of FIG. 1. More specifically, theaerial vehicles may be physical implementations of plurality of unmannedaerial vehicles 106 of FIG. 1. Although plurality of points 210 aredescribed as a plurality of unmanned aerial vehicles, in someillustrative examples, plurality of points 210 may represent anyquantity of conventional aircraft in place of or in addition to unmannedaerial vehicles. Although plurality of points 210 are depicted in atwo-dimensional setting, in a three-dimensional setting, plurality ofpoints 210 also include an altitude for each unmanned aerial vehicle ofthe plurality of unmanned aerial vehicles.

View 212 of region 200 is a snapshot view at a first time, such as firsttime 126 of FIG. 1. Plurality of points 210 will be positioned atdifferent locations within region 200 at a second time (not depicted).

In some illustrative examples, view 212 is an exemplary presence ofunmanned aerial vehicles employed by a company using large numbers ofunmanned aerial vehicles for operations. In these illustrative examples,unmanned aerial vehicles may be employed by a company delivering cargoin a city. In some illustrative examples, view 212 is an exemplarypresence of unmanned aerial vehicles employed by several operators.

Due to the quantity of unmanned aerial vehicles operating within region200, a good coverage of region 200 can be generated. Sensors connectedto the unmanned aerial vehicles represented by plurality of points 210create measurements for determining wind vectors within region 200.

Turning now to FIG. 3, an illustration of a three-dimensional view oflocations for a plurality of aerial vehicles identified in a region isdepicted in accordance with an illustrative embodiment. View 300 is athree-dimensional view of region 200 of FIG. 2.

As can be seen in view 300, marker 202 is one of series of stackedmarkers 302 extending from ground 304 upward in direction 306. As can beseen in view 300, marker 204 is one of series of stacked markers 308extending from ground 304 upward in direction 306. As can be seen inview 300, marker 206 is one of series of stacked markers 310 extendingfrom ground 304 upward in direction 306. As can be seen in view 300,marker 208 is one of series of stacked markers 312 extending from ground304 upward in direction 306.

In view 300, plurality of points 210 is present in a three-dimensionalspace. Plurality of points 210 represents positions of unmanned aerialvehicles flying within region 200 including altitudes 314, such asaltitudes 132 of FIG. 1. Although plurality of points 210 are describedas a plurality of unmanned aerial vehicles, in some illustrativeexamples, plurality of points 210 may represent any quantity ofconventional aircraft in place of or in addition to unmanned aerialvehicles.

Turning now to FIG. 4, an illustration of a three-dimensional view ofdetermined wind vectors in a region is depicted in accordance with anillustrative embodiment. In view 400, plurality of points 210 arereplaced by wind vectors 402. Each of wind vectors 402 represents windvectors determined by a wind speed calculator, such as wind speedcalculator 108 of FIG. 1. Each of wind vectors 402 includes a wind speedand a wind direction. Each of wind vectors 402 is associated with apoint of plurality of points 210.

Turning now to FIG. 5, an illustration of an unmanned aerial vehiclewith exemplary vectors is depicted in accordance with an illustrativeembodiment. Unmanned aerial vehicle 500 is a physical implementation ofan unmanned aerial vehicle of plurality of unmanned aerial vehicles 106of FIG. 1.

In view 502, unmanned aerial vehicle 500 has vector 504 representing aspeed and a heading selected by unmanned aerial vehicle 500. The speedand the heading selected by unmanned aerial vehicle 500 may be part of aflight plan followed by unmanned aerial vehicle 500.

In view 502, unmanned aerial vehicle 500 has vector 506 representing aspeed and a heading above ground. The speed and heading represented byvector 506 may be determined through usage of a GPS system.

Using vector addition, vector 508 is determined. Vector 508 representswind speed and wind direction.

The wind speed and the wind direction represented by vector 508 may bedetermined by wind speed calculator 108 of FIG. 1 using vector addition.The wind speed and wind direction represented by vector 508 may bestored in database 144 of FIG. 1. The wind speed and wind directionrepresented by vector 508 may be used to form three-dimensional model156 of FIG. 1.

Turning now to FIG. 6, an illustration of a two-dimensional view of setgrid points in a region is depicted in accordance with an illustrativeembodiment. As depicted, view 600 of region 200 is bounded by marker202, marker 204, marker 206, and marker 208. In view 600, set gridpoints 602 are positioned within region 200. As depicted, set gridpoints 602 are spaced regularly within region 200.

Turning now to FIG. 7, an illustration of a three-dimensional view ofset grid points in a region is depicted in accordance with anillustrative embodiment.

View 700 is a three-dimensional view of region 200 of FIG. 2 with setgrid points 602. As can be seen in view 700, set grid points 602 isthree-dimensional grid 702. Three-dimensional grid 702 is regularlyspaced in direction 704, direction 706, and direction 708.

Set grid points 602 form three-dimensional grid 702. Three-dimensionalgrid 702 is defined in lateral and vertical dimensions.Three-dimensional grid 702 explicitly defines locations bylatitude/longitude/altitude.

Turning now to FIG. 8, an illustration of a two-dimensional view ofinterpolated wind vectors at set grid points in a region is depicted inaccordance with an illustrative embodiment. In view 800, set grid points602 are replaced by interpolated wind vectors 802.

Each of interpolated wind vectors 802 represents wind vectors determinedby a three-dimensional model, such as three-dimensional model 156 ofFIG. 1. Each of interpolated wind vectors 802 includes a wind speed anda wind direction. Each of interpolated wind vectors 802 is associatedwith a point of set grid points 602. Although interpolated wind vectors802 are only depicted in a two-dimensional view on a lateral scale,interpolated wind vectors 802 may also be calculated at differentaltitudes.

Turning now to FIG. 9, an illustration of a two-dimensional view of anunmanned aerial vehicle with an original flight plan and a new flightplan taking into account micro wind conditions is depicted in accordancewith an illustrative embodiment. Unmanned aerial vehicle 900 operateswithin region 902. In this illustrative example, region 902 includesbuildings 904. In this illustrative example, unmanned aerial vehicle 900has destination 906. Path 908 is an initial path. Path 908 may bedetermined using any desirable method. In some illustrative examples,path 908 may be the fastest path without winds. In some illustrativeexamples, path 908 may be the most direct path.

Path 910 is a modified flight path, such as modified flight plan 172 ofFIG. 1. In this illustrative example, path 910 is created based on windvectors 912 in region 902. In some illustrative examples, wind vectors912 are determined in real-time. In some illustrative examples, whenwind vectors 912 are determined in real-time, wind vectors 912 may bedirectly measured by unmanned aerial vehicles. For example, wind vectors912 may be examples of wind measurements 128 of FIG. 1. In someillustrative examples, when wind vectors 912 are determined inreal-time, wind vectors 912 may be indirectly determined from observedspeeds and observed headings, such as observed speeds 138 and observedheadings 140 of FIG. 1. In some illustrative examples, unmanned aerialvehicle 900 may contribute measurements to wind vectors 912. In someother illustrative examples, unmanned aerial vehicle 900 does notcontribute measurements to wind vectors 912.

In other illustrative examples, wind vectors 912 are interpolated fromwind vectors determined. In these illustrative examples, wind vectors912 may be examples of interpolated wind vectors 164 of FIG. 1. Whenwind vectors 912 are interpolated from wind vectors determined, windvectors 912 are interpolated using wind vectors determined usingmeasurements taken within region 902. In some illustrative examples, themeasurements are taken by other unmanned aerial vehicles than unmannedaerial vehicle 900. In some illustrative examples, unmanned aerialvehicle 900 took at least one measurement of the measurements withinregion 902 used to form wind vectors 912.

In yet other illustrative examples, wind vectors 912 are generated by athree-dimensional wind prediction map, such as three-dimensional windprediction map 168 of FIG. 1. In these illustrative examples, windvectors 912 are generated when a coarsely-grained forecast, such ascoarsely grained forecast 150 of FIG. 1, is provided to athree-dimensional wind prediction map generator, such asthree-dimensional wind map generator 160 of FIG. 1.

Path 910 may be generated to decrease flight time to destination 906.Path 910 may be generated to decrease turbulence experienced by unmannedaerial vehicle 900. Path 910 may be generated to increase fuelefficiency of unmanned aerial vehicle 900.

Turning now to FIG. 10, an illustration of differences between a desiredpath and an actual path for an unmanned aerial vehicle is depicted inaccordance with an illustrative embodiment. Path 1000 is a desired pathfor unmanned aerial vehicle 1002. Unmanned aerial vehicle 1002 is aphysical implementation of one of plurality of unmanned aerial vehicles106 of FIG. 1. Wind vectors 1004 are changes to winds that have not beenidentified by other plurality of unmanned aerial vehicles. In attemptingto fly along path 1000, unmanned aerial vehicle 1002 will actuallyfollow path 1006 due to wind vectors 1004. Although path 1000 and path1006 are described as for unmanned aerial vehicle 1002, paths may alsobe generated for conventional aircraft.

Wind vectors 1004 may be reported to system 102 using measurements fromunmanned aerial vehicle 1002. In some illustrative examples, themeasurements may be direct measurements of wind vectors 1004 using asensor (not depicted) on unmanned aerial vehicle 1002. In someillustrative examples, the measurements may be indirect measurements ofwind vectors 1004 by directly measuring path 1000 and path 1006.

In this illustrative example, unmanned aerial vehicle 1002 is in-flight.To correct for wind vectors 1004 encountered during flight, unmannedaerial vehicle 1002 will try to return to path 1000. During flight,unmanned aerial vehicle 1002 sends measurements related to wind vectors1004 such that other unmanned aerial vehicles (not depicted) mayanticipate and compensate for wind vectors 1004 prior to encounteringwind vectors 1004. In some illustrative examples, measurements taken byunmanned aerial vehicle 1002 may be used by other unmanned aerialvehicles (not depicted) to identify paths that avoid wind vectors 1004.

The different components shown in FIGS. 2-10 may be combined withcomponents in FIG. 1, used with components in FIG. 1, or a combinationof the two. Additionally, some of the components in FIGS. 2-10 may beillustrative examples of how components shown in block form in FIG. 1can be implemented as physical structures.

Turning now to FIGS. 11A and 11B, an illustration of a flowchart of amethod for flying an unmanned aerial vehicle in a region based on windvectors determined in the region is depicted in accordance with anillustrative embodiment. Method 1100 may be implemented using system 102of FIG. 1. Method 1100 may be used to determine wind vectors, such aswind vectors 111, interpolated wind vectors 164, or predicted windvectors 170 of FIG. 1. Method 1100 may be used to fly unmanned aerialvehicle 116 in region 104 of FIG. 1. Method 1100 may be used in region200 of FIGS. 2-4 and FIGS. 6-8. Method 1100 may be used to fly unmannedaerial vehicle 500 of FIG. 5. Method 1100 may be used to plan path 910of FIG. 9.

Method 1100 collects altitude measurements for a plurality of aerialvehicles while the plurality of aerial vehicles is flying in a region(operation 1102). In some illustrative examples, the region is within asingle 0.5° lateral resolution grid. In some illustrative examples, theregion is at least one of a suburban region or an urban region.

Method 1100 determines wind vectors within the region using theplurality of aerial vehicles (operation 1104). In some illustrativeexamples, wind vectors are determined directly within the region usingwind sensors on the plurality of aerial vehicles. In these illustrativeexamples, the wind vectors are determined using wind measurements takenfrom wind sensors on the plurality of aerial vehicles.

In some other illustrative examples, the wind vectors are determinedindirectly within the region using measurements from sensors attached tothe plurality of aerial vehicles. In these illustrative examples, method1100 may collect set speeds and set headings for the plurality of aerialvehicles (operation 1106). In these illustrative examples, method 1100also collects observed speeds and observed headings for the plurality ofaerial vehicles, wherein determining the wind vectors comprisesdetermining the wind vectors within the region using vector addition,the set speeds, the set headings, the observed speeds, and the observedheadings (operation 1108).

Method 1100 plans a flight plan within the region for an aerial vehiclebased on the wind vectors determined (operation 1110). In someillustrative examples, the aerial vehicle is an unmanned aerial vehicle.In some illustrative examples, wherein the aerial vehicle is an unmannedaerial vehicle and wherein planning the flight plan within the regionfor the aerial vehicle comprises: determining a maximum acceptableturbulence for cargo of the unmanned aerial vehicle; and planning theflight plan such that the unmanned aerial vehicle is projected toencounter turbulence below the maximum acceptable turbulence (operation1112). In some illustrative examples, wherein the aerial vehicle is anunmanned aerial vehicle and wherein planning the flight plan within theregion for the aerial vehicle comprises: determining a deliver by timefor cargo of the unmanned aerial vehicle; and planning the flight plansuch that the unmanned aerial vehicle is projected to deliver the cargoprior to the deliver by time (operation 1114).

In some illustrative examples, planning the flight plan within theregion for the aerial vehicle based on the wind vectors determinedcomprises creating a modified flight plan for the aerial vehicle whilethe aerial vehicle is actively flying (operation 1116). In theseillustrative examples, the modified flight plan takes into account anydesirable parameters for at least one of the aerial vehicle or cargocarried by the aerial vehicle.

Method 1100 flies the unmanned aerial vehicle within the regionaccording to the flight plan (operation 1118). To fly the unmannedaerial vehicle within the region, the flight plan is communicated to theunmanned aerial vehicle by a communications system operably connected toa real-time wind analysis apparatus, such as real-time wind analysisapparatus 154 of FIG. 1.

In some illustrative examples, method 1100 creates a three-dimensionalwind map of the region with interpolated wind vectors determined basedon the wind vectors determined (operation 1120). In some illustrativeexamples, method 1100 receives a coarsely-grained forecast for theregion for a future time (operation 1122). In these illustrativeexamples, method 1100 may generate a three-dimensional wind predictionmap for the region for the future time using a three-dimensional modelof the region and the coarsely-grained forecast, wherein planning theflight path within the region comprises planning the flight path usingthe three-dimensional wind prediction map for the region for the futuretime (operation 1124).

Turning now to FIG. 12, an illustration of a flowchart of a method forflying an unmanned aerial vehicle in a region is depicted in accordancewith an illustrative embodiment. Method 1200 may be implemented usingsystem 102 of FIG. 1. Method 1200 may be used to determine wind vectors,such as wind vectors 111, interpolated wind vectors 164, or predictedwind vectors 170 of FIG. 1. Method 1200 may be used to fly unmannedaerial vehicle 116 in region 104 of FIG. 1. Method 1200 may be used inregion 200 of FIGS. 2-4 and FIGS. 6-8. Method 1200 may be used to flyunmanned aerial vehicle 500 of FIG. 5. Method 1200 may be used to planpath 910 of FIG. 9.

Method 1200 determines wind vectors within a region at a first timeusing a plurality of aerial vehicles (operation 1202). In someillustrative examples, wind vectors are determined directly within theregion using wind sensors on the plurality of aerial vehicles. In theseillustrative examples, the wind vectors are determined using windmeasurements taken from wind sensors on the plurality of aerialvehicles.

In some other illustrative examples, the wind vectors are determinedindirectly within the region using measurements from sensors attached tothe plurality of aerial vehicles. In some illustrative examples,determining the wind vectors within the region at a first time comprisesdetermining the wind vectors using vector addition, set speeds for theplurality of aerial vehicles in the region, set headings for theplurality of aerial vehicles, observed speeds for the plurality ofaerial vehicles, and observed headings for the plurality of aerialvehicles (operation 1203).

Method 1200 generates a three-dimensional wind map of the regionincluding interpolated wind vectors based on the wind vectors (operation1204). Method 1200 correlates the three-dimensional wind map with acoarsely-grained forecast for the region at the first time (operation1206). Method 1200 trains a three-dimensional model of the region withthe three-dimensional wind map correlated with the coarsely-grainedforecast for the region at the first time (operation 1208). Method 1200receives a coarsely-grained forecast for the region at a second time(operation 1210). Method 1200 creates a three-dimensional windprediction map for the region at the second time (operation 1212).Method 1200 flies an aerial vehicle based on the three-dimensional windprediction for the region at the second time (operation 1214).

In some illustrative examples, flying the aerial vehicle based on thethree-dimensional wind prediction for the region at the second timecomprises modifying a flight plan that the aerial vehicle is activelyflying (operation 1216). In some illustrative examples, flying theaerial vehicle based on the three-dimensional wind prediction for theregion at the second time comprises creating a new flight plan for theaerial vehicle prior to takeoff (operation 1218).

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatus and methods in an illustrativeembodiment. In this regard, each block in the flowcharts or blockdiagrams may represent a module, a segment, a function, and/or a portionof an operation or step.

In some alternative implementations of an illustrative embodiment, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession may be executed substantially concurrently, or the blocks maysometimes be performed in the reverse order, depending upon thefunctionality involved. Also, other blocks may be added, in addition tothe illustrated blocks, in a flowchart or block diagram.

In some illustrative examples, not all blocks of method 1100 areperformed. For example, at least one of operation 1106, operation 1108,operation 1120, or operation 1122 are optional. In some illustrativeexamples, not all blocks of method 1200 are performed. For example, atleast one of operation 1216 or operation 1218 are optional.

The illustrative examples provide a means to establish afour-dimensional weather model. The four-dimensional weather model ofthe illustrative examples is able to predict winds in lateral andvertical terms. In some illustrative examples, the four-dimensionalweather model is also able to predict disturbances or turbulence inlateral and vertical terms.

Instead of relying on coarsely-grained “macro” weather forecasts, theillustrative examples generate, for a limited three-dimensional space, amore detailed picture of microscopic winds in this space. These windsare able to be predicted ahead of an arbitrary point in time. The windscan be used to create flight plans that, due to the finer-grained natureof the weather forecasts of the illustrative examples, take into accountthe more realistic wind and precipitation conditions.

By taking into account the more realistic wind and precipitationconditions, an operator with a multitude of drones will be able toexperience less unforeseen disruptions to operations. Reducingunforeseen disruptions to operations thereby increases the operator'sreliability and therefore its own customer satisfaction. This methodrelies on an “Internet of Things”-system, specifically the drones of theoperator themselves, as well as any and all available measurements onmeteorological conditions.

Drones may fly significantly shorter distances than commercial aircraft,with the details of the weather not known, as the conventional forecastis too coarse. The illustrative examples fill this gap, as they modelthe wind situation in a limited, pre-defined space (e.g. a city in whicha drone operator operates and delivers its products).

The illustrative examples provide two main benefits: first, a droneoperator is able to determine the current condition of winds in athree-dimensional area. Determining the current condition of winds helpswith awareness of the current wind situation. Further, the illustrativeexamples are able to generate a predicted three-dimensional wind view.The finer resolution is more useful to drone flights in this area thanonly relying on the coarse NWS forecasts. Drone flight plans maytherefore be closer to the true trajectory and/or flight timeprescribed, thus increasing predictability. The drone operator may beable to provide a higher accuracy to its customers in turn, by moreaccurately predicting when a product will be delivered to the customer.

Also, with this information on microscopic winds, the drone operator isable to fly routes that are more energy-efficient. This results inlesser energy consumption and hence, less costs.

The illustrative examples may also provide benefits with turbulencemeasurements. Associating turbulence with four-dimensional locations canbring benefit to drone flight planning. Too many disturbances in flightmay not be cost-efficient. Additionally, too many disturbances in flightmay damage cargo, depending on the cargo the drone is carrying.

In general, acceptable levels of disturbances can be tied to the loadcarried. Some loads might only receive a certain amount of turbulence,otherwise cargo might be damaged. If applying this concept to “flyingtaxis,” the route can be determined based on the values of travel timein combination with passenger comfort as well. In general, a livere-planning is also supported and can be tied to the unmanned aerialvehicle knowing the allowed parameters for whatever is carried.

Knowing winds on a finer scale/resolution may be more beneficial thanrelying on a coarse grid, as it better reflects the true wind speedsituation. For airframers, this fine granular resolution would be oflimited benefit during long range cruise considering their size andspeed. However, for smaller vehicles like unmanned aerial vehicles(UAVs) or “flying taxis” that are a lot smaller than commercialaircraft, have a significantly smaller range of operations, and moveslower, a higher resolution for weather information is of significantbenefit.

The description of the different illustrative embodiments has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different illustrativeembodiments may provide different features as compared to otherillustrative embodiments. The embodiment or embodiments selected arechosen and described in order to best explain the principles of theembodiments, the practical application, and to enable others of ordinaryskill in the art to understand the disclosure for various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed is:
 1. A system for taking into account micro windconditions in a region a plurality of aerial vehicles within the region,each of the plurality of aerial vehicles having an altitude sensor and aGPS receiver; and a wind speed calculator, the wind speed calculatorconfigured to determine wind vectors within the region usingmeasurements from the plurality of aerial vehicles.
 2. The system ofclaim 1, wherein the plurality of aerial vehicles is a plurality ofunmanned aerial vehicles.
 3. The system of claim 1, wherein the windspeed calculator is configured to determine the wind vectors usingvector addition, set speeds of the plurality of aerial vehicles, setheadings of the plurality of aerial vehicles, observed speeds of theplurality of aerial vehicles, and observed headings of the plurality ofaerial vehicles.
 4. The system of claim 1 further comprising: athree-dimensional wind map generator configured to create athree-dimensional wind map with interpolated wind vectors within theregion, wherein the interpolated wind vectors are associated with setgrid points of a three-dimensional grid.
 5. The system of claim 4,wherein the three-dimensional wind map generator is further configuredto generate a three-dimensional wind prediction map for the region for afuture time using a three-dimensional model of the region and acoarsely-grained forecast for the future time.
 6. The system of claim 1further comprising: a flight plan generator configured to create aflight plan within the region for an aerial vehicle based on the windvectors determined.
 7. The system of claim 6, wherein the flight plangenerator is configured to create the flight plan using thethree-dimensional wind prediction map for the region for the futuretime.
 8. The system of claim 6 further comprising: a communicationsystem configured to communicate flight plans with the plurality ofaerial vehicles.
 9. The system of claim 6, wherein the flight plangenerator is configured to determine a maximum acceptable turbulence forcargo of the aerial vehicle and plan the flight plan such that theaerial vehicle is projected to encounter turbulence below the maximumacceptable turbulence.
 10. The system of claim 6, wherein the flightplan generator is configured to determine a deliver by time for cargo ofthe aerial vehicle, and plan the flight plan such that the aerialvehicle is projected to deliver the cargo prior to the deliver by time.11. The system of claim 6, wherein the aerial vehicle is an unmannedaerial vehicle.
 12. The system of claim 1 further comprising: a modeltraining system configured to refine and update a three-dimensionalmodel of the region using additional determined wind vectors andreceived coarsely-grained forecasts for the region.
 13. The system ofclaim 1, wherein the region is within a single 0.5° lateral resolutiongrid.
 14. A method comprising: collecting altitude measurements for aplurality of aerial vehicles while the plurality of aerial vehicles isflying in a region; and determining wind vectors within the region usingthe plurality of aerial vehicles.
 15. The method of claim 14 furthercomprising: planning a flight plan within the region for an aerialvehicle based on the wind vectors determined.
 16. The method of claim 15further comprising: flying the aerial vehicle within the regionaccording to the flight plan.
 17. The method of claim 16, wherein theaerial vehicle is an unmanned aerial vehicle.
 18. The method of claim17, wherein planning the flight plan within the region for the aerialvehicle comprises: determining a maximum acceptable turbulence for cargoof the unmanned aerial vehicle; and planning the flight plan such thatthe unmanned aerial vehicle is projected to encounter turbulence belowthe maximum acceptable turbulence.
 19. The method of claim 17, whereinplanning the flight plan within the region for the aerial vehiclecomprises: determining a deliver by time for cargo of the unmannedaerial vehicle; and planning the flight plan such that the unmannedaerial vehicle is projected to deliver the cargo prior to the deliver bytime.
 20. The method of claim 15, wherein planning the flight planwithin the region for the aerial vehicle based on the wind vectorsdetermined comprises creating a modified flight plan for the aerialvehicle while the aerial vehicle is actively flying.
 21. The method ofclaim 14 further comprising: collecting set speeds and set headings forthe plurality of aerial vehicles; and collecting observed speeds andobserved headings for the plurality of aerial vehicles, whereindetermining the wind vectors comprises determining the wind vectorswithin the region using vector addition, the set speeds, the setheadings, the observed speeds, and the observed headings.
 22. The methodof claim 14, wherein the region is within a single 0.5° lateralresolution grid.
 23. The method of claim 22, wherein the region is atleast one of a suburban region or an urban region.
 24. The method ofclaim 14 further comprising: creating a three-dimensional wind map ofthe region with interpolated wind vectors determined based on the windvectors determined.
 25. The method of claim 14 further comprising:receiving a coarsely-grained forecast for the region for a future time;and generating a three-dimensional wind prediction map for the regionfor the future time using a three-dimensional model of the region andthe coarsely-grained forecast, wherein planning the flight path withinthe region comprises planning the flight path using thethree-dimensional wind prediction map for the region for the futuretime.
 26. A method comprising: determining wind vectors within a regionat a first time using a plurality of aerial vehicles flying in theregion; generating a three-dimensional wind map of the region includinginterpolated wind vectors based on the wind vectors; correlating thethree-dimensional wind map with a coarsely-grained forecast for theregion at the first time; training a three-dimensional model of theregion with the three-dimensional wind map correlated with thecoarsely-grained forecast for the region at the first time; receiving acoarsely-grained forecast for the region at a second time; and creatinga three-dimensional wind prediction map for the region at the secondtime.
 27. The method of claim 26, wherein the aerial vehicle is anunmanned aerial vehicle.
 28. The method of claim 26, wherein determiningthe wind vectors within the region at a first time comprises determiningthe wind vectors using vector addition, set speeds for the plurality ofaerial vehicles in the region, set headings for the plurality of aerialvehicles, observed speeds for the plurality of aerial vehicles, andobserved headings for the plurality of aerial vehicles.
 29. The methodof claim 26 further comprising: flying an aerial vehicle based on thethree-dimensional wind prediction map for the region at the second time.30. The method of claim 29, wherein flying the aerial vehicle based onthe three-dimensional wind prediction map for the region at the secondtime comprises modifying a flight plan the aerial vehicle is activelyflying.
 31. The method of claim 29, wherein flying the aerial vehiclebased on the three-dimensional wind prediction map for the region at thesecond time comprises creating a new flight plan for the aerial vehicleprior to takeoff.